Biology subjects

Johnson, M. E.

Publications and source records attributed to Johnson, M. E..

At least 19 recordsLinked to original sources

bioRxiv2026

Synaptic proteomics identifies cathepsin B as a regulator of synapse remodelling during learning

Learning requires dynamic changes in synaptic protein composition. Most synaptic proteomic studies capture endpoint snapshots following training, overlooking molecular changes occurring during learning itself. We previously established TurboID proximity proteomics as an approach to capture protein-level changes during learning in the nervous system of Caenorhabditis elegans. Here, we apply this strategy to synapses. Trained synapses exhibited a distinct proteome compared with mock-trained controls, with pathways enriched for neurotransmission, synaptic reorganisation, protein trafficking, and autophagy. Enrichment of autophagy proteins led us to investigate the cathepsin B orthologue CPR-4, a previously uncharacterised candidate for learning identified exclusively in trained synapses. Functional validation identified CPR-4 as a novel regulator of memory: CPR-4 is required for learning, and the absence of CPR-4 results in trained synaptic proteomes that are more like those of mock-trained synapses. Our findings support a model through which CPR-4 promotes synaptic remodelling required for learning through targeted protein clearance.

neuroscience↗

bioRxiv2026

Defining reversible binding rates in 1D systems dependent on diffusion, density, and excluded volume

Binding reactions in effectively one-dimensional systems, such as proteins diffusing along DNA or other filaments, pose a fundamental coarse-graining challenge because stochastic trajectories are recurrent in one dimension and therefore do not admit a unique, separation-independent macroscopic association rate. As a result, continuum rate equations are not exact in 1D even for initially homogeneous systems. Here we develop a practical framework for mapping stochastic 1D reaction-diffusion dynamics onto effective kinetic models. Using mean-first-passage arguments and particle-based simulations, we define a density-dependent association rate and a corresponding single-rate approximation, and quantify when each provides an accurate description of the underlying stochastic dynamics. We implement 1D reaction-diffusion with excluded volume in the NERDSS software using a free-propagator reweighting algorithm and validate it against known pairwise and many-body limits. Our results show that ordinary rate equations with a single effective rate can accurately reproduce 1D reaction kinetics when the dimensionless parameter governing the ratio of intrinsic to diffusion-limited reactivity is small, with excellent agreement in the strongly rate-limited regime and increasing deviations as diffusion control strengthens. We further show that excluded volume in 1D can appreciably alter both kinetics and equilibrium populations, even at modest particle densities, by reducing accessible length and introducing blockade effects. Together, these results provide quantitative guidance for selecting between spatial simulations, density-dependent rate models, and single-rate continuum descriptions of reversible 1D binding reactions.

biophysics↗

bioRxiv2026

Structural dynamics insights into principles underlying the fitness of new broadly potent AAVs

Adeno-associated virus (AAV) is a leading platform for gene therapy, but current clinical-stage vectors require high doses associated with adverse events. Engineering of AAVs has produced more efficient vectors, although the mechanism underlying these improvements often remains poorly understood, limiting further development and raising potential safety concerns. Here, we leveraged a new workflow for AAV engineering with single-cell resolution, called scAAVengr-Hunt, to create best-in-class AAVs for gene delivery. ATX002, the top-performing vector, demonstrates broad potency across species, including nonhuman primate, mouse, and human, as well as across retina and brain. To understand the mechanism underlying this broad potency, we performed molecular dynamics simulations comparing AAV variants spanning a range of fitness levels. Structural dynamics analysis revealed a bifunctional molecular mechanism that confers potency through increased affinity of the capsid to the AAV receptor and regulation of heparan sulfate binding. This work provides critical insights relating structural mechanism to the fitness of engineered AAVs and establishes rich new avenues for AAV engineering through the integration of sequence-level analysis with computational biophysics.

bioengineering↗

bioRxiv2026

Clathrin is an Intrinsic Driver of Membrane Fission

Clathrin-mediated endocytosis is a primary internalization pathway where adaptor proteins recruit clathrin and bend the plasma membrane to form clathrin coated vesicles. Since clathrin does not bind membranes directly, its contributions to membrane remodeling remain unclear. Using an in vitro system that recruits clathrin directly to lipid membranes, we show that clathrin alone can drive end-to-end membrane budding and vesicle fission. Surprisingly, stronger clathrin assembly (i.e., higher membrane association) yields weaker membrane fission, indicating that membrane remodeling depends not simply on how much clathrin is recruited, but on how the resulting lattice is assembled. Brownian dynamics simulations, together with experimentally measured shifts in clathrin cage diameter, suggest that this counterintuitive behavior arises at least in part from assembly-dependent changes in intrinsic lattice curvature. This framework extends to adaptor-mediated recruitment, with clathrin either assisting or opposing membrane fission in an adaptor-dependent manner. Together, these findings support a mechanism by which coat protein mechanics regulate membrane remodeling.

biophysics↗

bioRxiv2026

Transforming macromolecular structures into simulations of self-assembly

Macromolecular self-assembly is a fundamental process in living and engineered systems, producing molecular machines like the ribosome or highly symmetric viral capsids. Thanks to sources like the Protein Data Bank (PDB) and AlphaFold3, the final target complexes are often known, but these static structures do not provide information on the self-assembly process directly. Computational models provide critical tools to study these essential pathways of self-assembly, but substantial coarse-graining of assembly subunits is necessary to achieve computational tractability of these relatively slow processes while retaining multi-valency. While rule-based or local interactions overcome the often-prohibitive enumeration of all possible assembly intermediates, they must ensure global structural constraints are met. We here demonstrate ioNERDSS, a user-friendly Python package that transforms 3D atomic structures into coarse-grained models for immediate simulation with the stochastic reaction-diffusion NERDSS software, converting static structures into time-resolved assembly trajectories. NERDSS uses rule-based interactions to simulate multi-component self-assembly at the minutes timescales and without limits to complex size or growth pathways. With ioNERDSS, each protein chain is defined by a rigid subunit with discrete interfaces and explicit orientational constraints that enforce a structured assembly. Repeated subunits (such as in viral capsids) are regularized to preserve the target topology across distinct stochastic assembly pathways, supporting assembly of structures with thousands of subunits. We initialize pairwise binding affinities using open-source machine-learned prediction tools, and our default coarse-grained (CG) models are all constrained by thermodynamic reversibility to reach an equilibrium steady-state. The binding rates and subunit abundances necessary to perform simulations are initialized at default values but represent the key variables (along with affinities) that cells and thus users would tune to control productive assembly. Benchmarking on over 40,000 PDB structures shows that the majority of CG models stochastically assemble into target structures. The ioNERDSS Python library links directly to open-source tools for visualization and analysis to facilitate fast and user-friendly structure validation and analysis of output for thermodynamic, kinetic, and nonequilibrium drivers of macromolecular self-assembly.

biophysics↗

bioRxiv2025

A membrane-driven biochemical oscillator tunable by the volume to surface area ratio

Oscillations are ubiquitous features of biological organisms, playing crucial roles in processes from circadian rhythms to developmental patterning. Protein-based biochemical oscillators have particular applications in synthetic biology because they can access fast and slow timescales that are independent from the transcription-translation machinery required of genetic oscillators. Here, we introduce and model such a mass-conserving biochemical oscillator using mass-action reaction kinetics that exploits dynamic changes to membrane phospholipid concentrations to drive proteins on and off the membrane in robust, tunable rhythms. Importantly, the oscillations rely on amplification of reactions on the membrane via dimensional reduction, and they are therefore tunable by variations in the volume-to-surface area ratio (V/A) of the system. With components inspired by the endocytic machinery, we show that a wide range of physiologically relevant biochemical rates can produce oscillations in part due to this independent geometric control. A broad computational screen of the high-dimensional parameter space reveals that oscillations require relatively strict enzyme kinetic design rules for low V/A but much more permissive kinetics for larger V/A. We validate that oscillations persist with more realistic reaction-diffusion simulations that captures explicit diffusion and stochastic, integer valued copy numbers, in overall good agreement with the period and amplitude of the deterministic oscillators. Because the oscillations rely on time-dependent changes to the surface properties and not post-translational modifications to the protein subunits, we demonstrate that it can be coupled to a self-assembling trimer, driving not only changes in localization but trimer yield. Our analysis establishes this membrane-localization oscillator as a new, geometry tunable and programmable timing module and suggests a potential for geometry sensing in engineered or cell-free systems.

biophysics↗

bioRxiv2025

Connectome-Based Predictive Modeling of Concurrent and Prospective Substance Use in Adolescence

Understanding the neural mechanisms of adolescent substance use is a critical public health issue, with direct implications for bolstering prevention and treatment strategies. Yet this effort is challenging because substance use is multi-faceted, commonly used brain network features are not optimized to capture both local and global aspects of intrinsic connectivity, and because substance use facets change over time. In this study, we operationalized adolescent substance use along three dimensions--intent, access, and family-developmental history--and trained predictive models of each facet using resting-state connectivity. Trait impulsivity, a known risk factor, was also examined. Using Baseline and 2 Year Follow-Up data from the ABCD Bids Community Collection (ABCC), we found that prediction was more successful at follow-up than baseline. At baseline, predictive accuracy was modest and intent to use substances was the most accurately predicted facet. Prediction accuracies at follow-up were much higher, with access and family-developmental history being better predicted, signaling a developmental shift in the brain-behavior mapping of substance use vulnerabilities. These findings suggest that the neurobiological correlates of substance use are dynamic across adolescence, possibly reflecting changing phenotype. More broadly, these results underscore the importance of modeling distinct substance use facets and accounting for developmental timing to understand risk trajectories, while contributing to a growing literature that shows early-developing individual differences are predictive of later outcomes.

neuroscience↗

bioRxiv2025

Membrane bending energy selects for compact growth of protein assemblies

Remodeling of cell membranes into vesicles is essential for receptor transport into cells and viral escape from infected cells. Membranes must be forced into these highly curved vesicles, and this is primarily driven through a structured assembly of multiple, multivalent interacting protein subunits forming a lattice. Lattice assembly from these subunits is a stochastic process, and intermediate structures formed during growth can vary in both structure and stability. Here we show that the membrane bending energy cost per protein rises significantly when remodeling is driven by lattice intermediates that deviate from compact, ideal spherical structures. We use a continuum membrane mechanics model coupled to lattice intermediates assembled from stochastic rigid-body simulations of HIV-1 Gag lattice assembly to quantify the bending energy as it systematically varies with lattice eccentricity. Our results show that highly eccentric lattices induce a higher bending energy cost because the lattices still deform the membrane into an approximate spherical cap, but the radius of the cap is larger due to the imperfect lattice geometry. These quantitative trends are also nearly independent of the density of links to the membrane, emphasizing the importance of the lattice perimeter shape instead. Rescaling thus recovers an approximately universal bending energy cost when evaluated relative to the circumscribing sphere of the lattice intermediates. These results show that assembly pathways coupled to membrane remodeling face much stronger selection pressure for highly compact growth compared to solution assembly pathways due to bending energy costs and provide a tool to characterize these pathways during processes like viral budding and endocytosis.

biophysics↗

bioRxiv2025

Dopaminergic Modulation of Short-Term Associative Memory in Caenorhabditis elegans

Forgetting, the inability to retrieve previously encoded memories, is an active process involving neurotransmission, second messenger signalling, and cytoskeletal modifications. Forgetting is thought to be essential to remove irrelevant memories and to increase the capacity to encode new memories. Therefore, identifying key regulators of active forgetting is crucial to advance our understanding of neuroplasticity. In this study, we utilised the compact and tractable Caenorhabditis elegans model to investigate the role of the neurotransmitter dopamine in forgetting. We conducted butanone associative learning assays based on an established protocol and used mutant strains deficient in dopamine synthesis (tyrosine hydroxylase CAT-2 and dopamine transporter DAT-1) and signalling (G protein-coupled receptors DOP-1, DOP-2, and DOP-3) to assess the impact on learning and memory retention. Learning was measured immediately post-training, and memory retention was evaluated every 0.5 hours up to 2 hours. Our results show that animals lacking dopamine display a modest enhancement in learning relative to wild-type, with the learned association persisting for at least 2 hours after training. We also found that D2-like receptors DOP-2 and DOP-3 act together to modulate the forgetting process, with D1-like receptor DOP-1 functioning redundantly. Furthermore, re-expression of CAT-2 tyrosine hydroxylase in ADE and/or CEP neurons was unable to rescue the memory retention phenotype observed in cat-2 mutants, suggesting that dopamine release from all dopaminergic neurons is required to modulate forgetting. These findings highlight the critical role of dopamine in forgetting, consistent with findings in Drosophila, and suggest potential relevance for understanding memory retention during healthy ageing and in conditions with dopamine imbalances such as Parkinsons disease.

neuroscience↗

bioRxiv2025

Mechanisms of enhanced or impaired DNA target selectivity driven by protein dimerization

Successful DNA transcription demands coordination between proteins that bind DNA while simultaneously binding to one another to form dimers or higher-order complexes. For proteins with numerous DNA targets throughout the genome, measurements that report on their dwell time or occupancy thus represent a convolution over a population interacting with specific DNA, nonspecific DNA, or protein partners on DNA. Dimerization is known to add contacts that can help a single protein to stably bind DNA. However, we show here that dimerization can also impair measured dwell times and occupancy on target sequences because the population redistributes across DNA. We combine mass-action kinetic models of pairwise reversible reactions between proteins and DNA with theory and spatial stochastic simulations to isolate the role of dimerization on observed DNA dwell times, occupancy, and spatial distribution of proteins on DNA. Three key themes emerge: (i) Protein-protein interactions, in addition to protein-DNA interactions, can localize a protein to DNA, and relative binding rates can thus widely tune dwell times. (ii) Dimensional reduction achieved through nonspecific binding and subsequent 1D diffusion controls the order-of-magnitude of enhancements despite nucleosome barriers. (iii) Dimerization enhances selectivity for locally clustered targets and often impairs binding to widely-spaced targets by sequestration. Compared with ChIP-seq data, our model explains how the distribution of the essential GAF protein throughout the genome is highly selective for clustered targets due to protein interactions. This model framework predicts when even weak dimerization can redistribute and stabilize proteins on DNA as a necessary part of transcription. SignificanceTranscription of DNA into RNA relies on a globally orchestrated but microscopically stochastic recruitment of multiple proteins to targeted DNA sequences. DNA binding is only a first step. Here we show how proteins with many targets throughout the genome can use protein-protein dimerization to not only help proteins bind DNA, but also to redistribute the population genome-wide, significantly enhancing or impairing dwell times and target occupancies dependent on target spacing. Using theory and simulations along with experimental comparison, outcomes depend on protein-to-target ratios, dimensional gains of 1D searchers, and relative binding rates. Our relatively simple formulas predict how these factors yield negligible or dramatic changes in target selection, with direct mapping to experimental measurements on essential transcription factors.

biophysics↗

bioRxiv2025

Binding affinities for 2D protein dimerization benefit from enthalpic stabilization

Dimerization underpins all macromolecular assembly processes both on and off the membrane. While the strength of dimerization, KD, is commonly quantified in solution (3D), many proteins like the soluble BAR domain-containing proteins also reversibly dimerize while bound to a membrane surface (2D). The ratio of dissociation constants, [Formula], defines a lengthscale that is essential for determining whether dimerization is more favorable in solution or on the membrane surface, particularly for these proteins that reversibly transition between 3D and 2D. While purely entropic rigid-body estimates of h apply well to transmembrane adhesion proteins, we show here using Molecular Dynamics simulations that even moderate flexibility in BAR domains dramatically alters the free energy landscape from 3D to 2D, driving enhanced selectivity and stability of the native dimer in 2D. By simulating BAR homodimerization in three distinct environments, 1) solution (3D), 2) bound to a lipid bilayer (2D), and 3) fully solvated but restrained to a pseudo membrane (2D), we show that both 2D environments induce backbone configurations that better match the crystal structure and produce more enthalpically favorable dimer states, violating the rigid-body estimates to drive h<<hRIGID. Remarkably, contact with an explicit lipid bilayer is not necessary to drive these changes, as the solvated pseudo membrane induces this same result. We show this outcome depends on the stability of the protein interaction, as a parameterization that produces exceptionally stable binding in 3D does not induce systematic improvements on the membrane. With h lengthscales calculated here that are well below a physiological volume-to-surface-area lengthscale, assembly will be dramatically enhanced on the membrane, which aligns with BAR domain function as membrane remodelers. Our approach provides simple metrics to move beyond rigid-body estimates of 2D affinities and assess whether conformational flexibility selects for enhanced stability on membranes.

biophysics↗

bioRxiv2024

The PH domain in the ArfGAP ASAP1 drives catalytic activation through an unprecedented allosteric mechanism

ASAP1 is a multidomain Arf GTPase-activating protein (ArfGAP) that catalyzes GTP hydrolysis on the small GTPase Arf1 and is implicated in cancer progression. The PH domain of ASAP1 enhances its activity greater than 7 orders of magnitude but the underlying mechanisms remain poorly understood. Here, we combined Nuclear Magnetic Resonance (NMR), Molecular Dynamic (MD) simulations and mathematical modeling of functional data to build a comprehensive structural-mechanistic model of the complex of Arf1 and the ASAP1 PH domain on a membrane surface. Our results support a new conceptual model in which the PH domain contributes to efficient catalysis not only by membrane recruitment but by acting as a critical component of the catalytic interface, binding Arf{middle dot}GTP and allosterically driving it towards the catalytic transition state. We discuss the biological implications of these results and how they may apply more broadly to poorly understood membrane-dependent regulatory mechanisms controlling catalysis of the ArfGAP superfamily as well as other peripheral membrane enzymes.

biochemistry↗

bioRxiv2024

Parallelization of particle-based reaction-diffusion simulations using MPI

Particle-based reaction-diffusion models offer a high-resolution alternative to the continuum reaction-diffusion approach, capturing the discrete and volume-excluding nature of molecules undergoing stochastic dynamics. These methods are thus uniquely capable of simulating explicit self-assembly of particles into higher-order structures like filaments, spherical cages, or heterogeneous macromolecular complexes, which are ubiquitous across living systems and in materials design. The disadvantage of these high-resolution methods is their increased computational cost. Here we present a parallel implementation of the particle-based NERDSS software using the Message Passing Interface (MPI) and spatial domain decomposition, achieving close to linear scaling for up to 96 processors in the largest simulation systems. The scalability of parallel NERDSS is evaluated for bimolecular reactions in 3D and 2D, for self-assembly of trimeric and hexameric complexes, and for protein lattice assembly from 3D to 2D, with all parallel test cases producing accurate solutions. We demonstrate how parallel efficiency depends on the system size, the reaction network, and the limiting timescales of the system, showing optimal scaling only for smaller assemblies with slower timescales. The formation of very large assemblies represents a challenge in evaluating reaction updates across processors, and here we restrict assembly sizes to below the spatial decomposition size. We provide the parallel NERDSS code open source, with detailed documentation for developers and extension to other particle-based reaction-diffusion software.

biophysics↗

bioRxiv2024

Variant-to-function mapping of late-onset Alzheimer's disease GWAS signals in human microglial cell models implicates RTFDC1 at the CASS4 locus

Late-onset Alzheimers disease (LOAD) research has principally focused on neurons over the years due to their known role in the production of amyloid beta plaques and neurofibrillary tangles. In contrast, recent genomic studies of LOAD have implicated microglia as culprits of the prolonged inflammation exacerbating the neurodegeneration observed in patient brains. Indeed, recent LOAD genome-wide association studies (GWAS) have reported multiple loci near genes related to microglial function, including TREM2, ABI3, and CR1. However, GWAS alone cannot pinpoint underlying causal variants or effector genes at such loci, as most signals reside in non-coding regions of the genome and could presumably confer their influence frequently via long-range regulatory interactions. We elected to carry out a combination of ATAC-seq and high-resolution promoter-focused Capture-C in two human microglial cell models (iPSC-derived microglia and HMC3) in order to physically map interactions between LOAD GWAS-implicated candidate causal variants and their corresponding putative effector genes. Notably, we observed consistent evidence that rs6024870 at the GWAS CASS4 locus contacted the promoter of nearby gene, RTFDC1. We subsequently observed a directionallly consistent decrease in RTFDC1 expression with the the protective minor A allele of rs6024870 via both luciferase assays in HMC3 cells and expression studies in primary human microglia. Through CRISPR-Cas9-mediated deletion of the putative regulatory region harboring rs6024870 in HMC3 cells, we observed increased pro-inflammatory cytokine secretion and decreased DNA double strand break repair related, at least in part, to RTFDC1 expression levels. Our variant-to-function approach therefore reveals that the rs6024870-harboring regulatory element at the LOAD CASS4 GWAS locus influences both microglial inflammatory capacity and DNA damage resolution, along with cumulative evidence implicating RTFDC1 as a novel candidate effector gene.

genetics↗

bioRxiv2024

Predicting how lipid composition controls protein curvature sensing with a continuum bilayer membrane model

Cytoplasmic proteins must recruit to membranes to function in processes such as endocytosis and cell division. Many of these proteins recognize not only the chemical structure of the membrane lipids, but the curvature of the surface, binding more strongly to more highly curved surfaces, or curvature sensing. Curvature sensing by amphipathic helices is known to vary with membrane bending rigidity, but changes to lipid composition can simultaneously alter membrane thickness, spontaneous curvature, and leaflet symmetry, thus far preventing a systematic characterization of lipid composition on such curvature sensing through either experiment or simulation. Here we develop and apply a bilayer continuum membrane model that can tractably address this gap, quantifying how controlled changes to each material property can favor or disfavor protein curvature sensing. We evaluate both energetic and structural changes to vesicles upon helix insertion, with strong agreement to new in vitro experiments and all-atom MD simulations, respectively. Our membrane model builds on previous work to include both monolayers of the bilayer via representation by continuous triangular meshes. We introduce a coupling energy that captures the incompressibility of the membrane and the established energetics of lipid tilt. In agreement with experiment, our model predicts stronger curvature sensing in membranes with distinct tail groups (POPC vs DOPC vs DLPC), despite having identical head-group chemistry; the model shows that the primary driving force for weaker curvature sensing in DLPC is that it is thinner, and more wedge shaped. Somewhat surprisingly, asymmetry in lipid shape composition between the two leaflets has a negligible contribution to membrane mechanics following insertion. Our multi-scale approach can be used to quantitatively and efficiently predict how changes to membrane composition in flat to highly curved surfaces alter membrane energetics driven by proteins, a mechanism that helps proteins target membranes at the correct time and place. SignificanceProteins must recruit to membranes for essential biological functions including endocytosis and cell division. In addition to recognizing specific lipid head-groups, many of these proteins also sense the curvature of the membrane, but the strength of sensing is known to vary with distinct membrane compositions. Predicting the dependence of sensing on changes to lipid composition cannot be done a priori due to the multiple material properties, including bilayer thickness, bending rigidity, tilt modulus, spontaneous curvature, and leaflet asymmetry that vary with lipid type. Here we use a multi-scale approach to systematically address this gap, developing a double-leaflet continuum model that is informed by structural deformations from all-atom MD and validated against in vitro experiments. This efficient approach can be applied and extended to quantify how proteins sense and drive membrane curvature across a wide range of membrane bilayers, including distinct leaflet compositions and membrane geometries.

biophysics↗

bioRxiv2023

Discovering optimal kinetic pathways for self-assembly using automatic differentiation

During self-assembly of macromolecules ranging from ribosomes to viral capsids, the formation of long-lived intermediates or kinetic traps can dramatically reduce yield of the functional products. Understanding biological mechanisms for avoiding traps and efficiently assembling is essential for designing synthetic assembly systems, but learning optimal solutions requires numerical searches in high-dimensional parameter spaces. Here, we exploit powerful automatic differentiation algorithms commonly employed by deep learning frameworks to optimize physical models of reversible self-assembly, discovering diverse solutions in the space of rate constants for 3-7 subunit complexes. We define two biologically-inspired protocols that prevent kinetic trapping through either internal design of subunit binding kinetics or external design of subunit titration in time. Our third protocol acts to recycle intermediates, mimicking energy-consuming enzymes. Preventative solutions via interface design are the most efficient and scale better with more subunits, but external control via titration or recycling are effective even for poorly evolved binding kinetics. Whilst all protocols can produce good solutions, diverse subunits always helps; these complexes access more efficient solutions when following external control protocols, and are simpler to design for internal control, as molecular interfaces do not need modification during assembly given sufficient variation in dimerization rates. Our results identify universal scaling in the cost of kinetic trapping, and provide multiple strategies for eliminating trapping and maximizing assembly yield across large parameter spaces. SIGNIFICANCEMacromolecular complexes are frequently composed of diverse subunits. While evolution may favor repeated subunits and symmetry, we show how diversity in subunits generates an expansive parameter space that naturally improves the expressivity of self-assembly, much like a deeper neural network. By using automatic differentiation algorithms commonly used in deep learning, we searched these parameter spaces to identify classes of kinetic protocols that mimic biological solutions for productive self-assembly. Our results reveal how high-yield complexes that easily become kinetically trapped in incomplete intermediates can instead be steered by internal design of rate constants or external and active control of subunits to efficiently assemble, exploiting nonequilibrium control of these ubiquitous dynamical systems.

biophysics↗

bioRxiv2023

Discovery and characterization of highly potent and selective covalent inhibitors of SARS-CoV-2 PLpro

Coronavirus infections, such as the global COVID-19 pandemic, have had a profound impact on many aspects of our daily life including working style, economy, and the healthcare system. To prevent the rapid viral transmission and speed up recovery from the infection, many academic organizations and industry research labs have conducted extensive research on discovering new therapeutic options for SARS-CoV-2. Among those efforts, RNA-dependent RNA polymerase (RdRp) inhibitors such as Remdesivir, Molnupiravir and 3CLpro inhibitor such as Nirmatrelvir (Paxlovid) have been widely used as the therapeutic options. Given the recent emergence of several new variants that caused a resurgence of the virus, it would be beneficial to discover more diverse therapeutic options with novel anti-viral mechanisms. In this regard, PLpro has been highlighted since it, along with 3CLpro, is one of the two most important proteases that are required for SARS-CoV-2 viral processing. While 3CLpro inhibitors were extensively investigated in the light of Emergency Use Authorizations of Nirmatrelvir, PLpro inhibitors have not been thoroughly investigated even preclinically. Thus, discovery efforts on antivirals acting against PLpro will be valuable. PLpro inhibitors may exert their activity by inhibiting viral replication and enhancing the host defense system through blocking virus-induced cell signaling events for evading host immune response. In this study, we report the discovery and development of two covalent irreversible PLpro inhibitors, HUP0109 and its deuterated analog DX-027, out of our quest for novel anti-COVID 19 therapeutic agents for the past two and half years. HUP0109 selectively targets the viral catalytic cleft of PLpro and covalently modifies its active site cysteine residue (C111). Promising results from preclinical evaluation suggest that DX-027 can be developed as a potential COVID-19 treatment.

pharmacology and toxicology↗

bioRxiv2023

Surveillance of Vermont wildlife in 2021-2022 reveals no detected SARS-CoV-2 viral RNA

Previous studies have documented natural infections of SARS-CoV-2 in various domestic and wild animals. More recently, studies have been published noting the susceptibility of members of the Cervidae family, and infections in both wild and captive cervid populations. In this study, we investigated the presence of SARS-CoV-2 in mammalian wildlife within the state of Vermont. 739 nasal or throat samples were collected from wildlife throughout the state during the 2021 and 2022 harvest season. Data was collected from red and gray foxes (Vulpes vulples and Urocyon cineroargentus, respectively), fishers (Martes pennati), river otters (Lutra canadensis), coyotes (Canis lantrans), bobcats (Lynx rufus rufus), black bears (Ursus americanus), and white-tailed deer (Odocoileus virginianus). Samples were tested for the presence of SARS-CoV-2 via quantitative RT-qPCR using the CDC N1/N2 primer set and/or the WHO-E gene primer set. Our results indicate that no sampled wildlife were positive for SARS-CoV-2. This finding is surprising, given that most published North America studies have found SARS-CoV-2 within their deer populations. The absence of SARS-CoV-2 RNA in populations sampled here may provide insights in to the various environmental and anthropogenic factors that reduce spillover and spread in North Americans wildlife populations.

microbiology↗